
# Import libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly
import plotly.express as px
import plotly.graph_objs as go
from plotly import tools
from plotly.offline import init_notebook_mode, plot, iplot


# Extract latitudes & longitudes of locations

import geopy
from geopy.geocoders import Nominatim
geolocator=Nominatim(user_agent="app")
location = geolocator.geocode("Slovakia")
print(location.latitude)
print(location.longitude)

48.7411522
19.4528646


# Import dataset

import ssl
ssl._create_default_https_context = ssl._create_unverified_context

current_data = pd.read_csv('https://raw.githubusercontent.com/datasets/covid-19/master/data/countries-aggregated.csv')
current_data.head()


# Choropleth map of confirmed cases in Europe

fig = px.choropleth(current_data,locations='Country',locationmode='country names',color='Confirmed',animation_frame='Date',scope='europe')
fig.update_layout(title='Choropleth map of confirmed cases in Europe',template="plotly_dark")
fig.show()


# Choropleth map of spread over time

fig = px.scatter_geo(current_data,locations='Country',locationmode='country names',color='Confirmed',size='Confirmed',hover_name="Country",animation_frame='Date',title='Spread over Time')
fig.update(layout_coloraxis_showscale=False,layout_template="plotly_dark")
fig.show()


# Create dataframe

df = current_data
df.shape

(55670, 5)


# Group by country

df2 = df.groupby(['Country'])[['Confirmed','Recovered','Deaths']].max().reset_index()
df2.head()


# Zoomed in on my homeland

df2[df2['Country']=='Slovakia']


# Daily updates

df_slovakia = df[df['Country']=='Slovakia']
df_slovakia.tail()


# COVID-19 confirmed, recovered cases with deaths in Slovakia

def show_plot(df, fig_title):
  df.plot(x = 'Date', figsize = (15,7), linewidth = 3, title = fig_title)
  plt.grid()
  plt.show()

show_plot(df_slovakia, "COVID-19 in Slovakia")


# COVID-19 deaths in Slovakia

df_slovakia.plot(x = 'Date', y = 'Deaths', figsize = (15,7), linewidth = 2);
plt.ylabel('Count')
plt.title('COVID-19 deaths in Slovakia')
plt.legend(loc="upper left")
plt.grid()


# Daily updates

df_us = df[df['Country']=='US']
df_us.tail()


# COVID-19 confirmed, recovered cases with deaths in the US

def show_plot(df, fig_title):
  df.plot(x = 'Date', figsize = (15,7), linewidth = 3, title = fig_title)
  plt.grid()
  plt.show()

show_plot(df_us, "COVID-19 in US")


# COVID-19 deaths in the US

df_slovakia.plot(x = 'Date', y = 'Deaths', figsize = (15,7), linewidth = 2);
plt.ylabel('Count')
plt.title('COVID-19 deaths in US')
plt.legend(loc="upper left")
plt.grid()


# Identify latitudes & longitudes of locations, append them to new dataframe and drop geo_loc

lat_lon=[]
geolocator=Nominatim(user_agent="app")

for location in df2['Country']:
    location = geolocator.geocode(location)

    if location is None:
        lat_lon.append(np.nan)
    else:    
        geo=(location.latitude,location.longitude)
        lat_lon.append(geo)
        
df2['geo_loc'] = lat_lon
lat,lon = zip(*np.array(df2['geo_loc']))
df2['Latitude']=lat
df2['Longitude']=lon
df2.drop(['geo_loc'],axis=1,inplace=True)
df2.head()


# Plotting a map of current confirmed cases

import folium

current_map = folium.Map(location=[54, 15], tiles='openstreetmap', zoom_start=2)

for idx, row in df2.iterrows():
    folium.Marker([row['Latitude'], row['Longitude']], popup=row['Recovered']).add_to(current_map)

current_map


# Plotting a map of current confirmed cases in clusters

current_map = folium.Map(location=[54,15], tiles='cartodbpositron', zoom_start=2)

from folium.plugins import MarkerCluster
mc = MarkerCluster()
for idx, row in df2.iterrows():
    mc.add_child(folium.Marker([row['Latitude'], row['Longitude']],popup=row['Confirmed']))
current_map.add_child(mc)

current_map


# Plotting a map of current confirmed cases with heat map

from folium.plugins import HeatMap

current_map = folium.Map(location=[54,15], zoom_start=2)
HeatMap(data=df2[['Latitude', 'Longitude','Confirmed']], radius=15).add_to(current_map)

current_map


# Import dataset

worldometer = pd.read_csv('~/Documents/Github/various-projects/data/worldometer_data.csv')
worldometer.head()


# Highlighting maximum values

# worldometer.style.background_gradient(cmap='RdPu')


# Creating function of comparision

def plot(df,x,y,xaxis_label,yaxis_label,title):
    fig = px.bar(worldometer.head(20), y=y,x=x,color='WHO Region')
    fig.update_layout(title=title,xaxis_title=xaxis_label,yaxis_title=yaxis_label)
    fig.show()


# Total Number of Cases

plot(worldometer,'Country/Region','TotalCases','Country','Deaths/Million','Total Number of Cases')


# Top 10 countries of "Deaths/1M pop" & "Tests/1M pop"

top10d_1m = worldometer.nlargest(n = 15, columns = "Deaths/1M pop")
top10t_1m = worldometer.nlargest(n = 15, columns = "Tests/1M pop")


# Show top 15 countries of "Deaths/1M pop"

plt.figure(figsize=(20, 7))
sns.barplot(x = 'Country/Region', y = "Deaths/1M pop", data = top10d_1m);


# Show top 15 countries of "Tests/1M pop"

plt.figure(figsize=(20, 7))
sns.barplot(x = 'Country/Region', y = "Tests/1M pop", data = top10t_1m);


# Slovakia

slovakia = worldometer[worldometer['Country/Region']=='Slovakia']
slovakia


# Show Slovakia

sns.set_style('darkgrid')
f, axes = plt.subplots(2, 2, figsize = (12,12))
a_1 = sns.barplot (data=slovakia, x = 'Country/Region', y = 'Population', ax =axes[0,0], color = "blue")
a_2 = sns.barplot (data=slovakia, x = 'Country/Region', y = 'TotalCases', ax =axes[0,1], color = "green")
a_3 = sns.barplot (data=slovakia, x = 'Country/Region', y = 'NewCases', ax =axes[1,0], color = "orange")
b_1 = sns.barplot (data=slovakia, x = 'Country/Region', y = 'TotalDeaths', ax =axes[1,1], color = "red")
plt.show()


# USA

usa = worldometer[worldometer['Country/Region']=='USA']
usa

	Date	Country
0	2020-01-22	Afghanistan
1	2020-01-23	Afghanistan
2	2020-01-24	Afghanistan
3	2020-01-25	Afghanistan
4	2020-01-26	Afghanistan

	Country	Confirmed	Recovered	Deaths
0	Afghanistan	42297	34721	1574
1	Albania	24731	12203	571
2	Algeria	62693	42325	2062
3	Andorra	5437	4332	75
4	Angola	12680	5927	308

	Date	Country	Confirmed	Recovered	Deaths
45410	2020-11-05	Slovakia	68734	16426	286
45411	2020-11-06	Slovakia	71088	16990	317
45412	2020-11-07	Slovakia	73667	18354	351
45413	2020-11-08	Slovakia	75495	19481	351
45414	2020-11-09	Slovakia	76072	19757	366

	Date	Country	Confirmed	Recovered	Deaths
51856	2020-11-05	US	9643540	3781751	235014
51857	2020-11-06	US	9752360	3810791	236168
51858	2020-11-07	US	9880828	3851465	237204
51859	2020-11-08	US	9999644	3881491	237666
51860	2020-11-09	US	10111077	3928845	238256

	Country	Confirmed	Recovered	Deaths	Latitude	Longitude
0	Afghanistan	42297	34721	1574	33.768006	66.238514
1	Albania	24731	12203	571	41.000028	19.999962
2	Algeria	62693	42325	2062	28.000027	2.999983
3	Andorra	5437	4332	75	42.540717	1.573203
4	Angola	12680	5927	308	-11.877577	17.569124

COVID-19 Analysis¶

Situation in Slovakia¶

Situation in the US¶

Situation around the world¶

Worldometer Analysis¶

	Country/Region	Continent	Population	TotalCases	NewCases	TotalDeaths	NewDeaths	TotalRecovered	NewRecovered	ActiveCases	Serious,Critical	Tot Cases/1M pop	Deaths/1M pop	TotalTests	Tests/1M pop	WHO Region
0	USA	North America	3.317063e+08	10557037	133427.0	245777.0	1322.0	6595348.0	41959.0	3715912.0	19077.0	31826.0	741.0	160475931.0	483789.0	Americas
1	India	Asia	1.384901e+09	8635754	44679.0	127615.0	511.0	8011844.0	54638.0	496295.0	8944.0	6236.0	92.0	119615857.0	86371.0	South-EastAsia
2	Brazil	South America	2.131064e+08	5701283	25517.0	162842.0	204.0	5064344.0	NaN	474097.0	8318.0	26753.0	764.0	21900000.0	102766.0	Americas
3	France	Europe	6.532608e+07	1829659	22180.0	42207.0	857.0	131920.0	2185.0	1655532.0	4750.0	28008.0	646.0	18249032.0	279353.0	Europe
4	Russia	Europe	1.459573e+08	1817109	20977.0	31161.0	368.0	1350741.0	15600.0	435207.0	2300.0	12450.0	213.0	65600000.0	449447.0	Europe